Animal shelters are stressful environments for dogs and a plethora of research has been conducted on interventions aimed at improving the welfare of these animals. One type of intervention is social interaction, either between dogs and people or dogs and conspecifics. To investigate the types of social interaction dogs engage in and the impact of that contact on their welfare, 12 dogs were enrolled to participate in group sessions with other dogs, supervised by staff, in a shelter setting. There were three, 15-minute sessions per day across three days in which groups of two to four dogs were observed and recorded on video. These videos were then analyzed per dog for three types of interactions: dog-dog, dog-human, and dog-environment. It was found that the dogs spent significantly more time engaging with the staff members in the room than with conspecifics or the environment. Physiological measurements, including cortisol and S-IgA levels, were taken using urinary and fecal samples obtained both in the morning prior to these interaction sessions and after the final interaction of the day. No significant correlations were found between the amount of time that the dogs spent in each type of interaction and dogs’ cortisol or S-IgA levels. However, smaller statistical effects suggest that human interaction may correspond with decreased stress the day after interaction while conspecific interaction may be related to increases in stress the following day. Overall, these findings suggest that social interaction, particularly with people, may be beneficial, and should be further explored as a method to enhance the well-being of shelter dogs.
Recent studies suggest a role for the microbiota in autism spectrum disorders (ASD), potentially arising from their role in modulating the immune system and gastrointestinal (GI) function or from gut–brain interactions dependent or independent from the immune system. GI problems such as chronic constipation and/or diarrhea are common in children with ASD, and significantly worsen their behavior and their quality of life. Here we first summarize previously published data supporting that GI dysfunction is common in individuals with ASD and the role of the microbiota in ASD. Second, by comparing with other publically available microbiome datasets, we provide some evidence that the shifted microbiota can be a result of westernization and that this shift could also be framing an altered immune system. Third, we explore the possibility that gut–brain interactions could also be a direct result of microbially produced metabolites.
Personality testing in dogs has become a controversial topic in the dog community in the last few years. These assessments have been used among owners, shelters, working dog trainers, breeders, and researchers to identify patterns of behavior that may lead to insight about a dog’s personality. Due to inconsistencies in terminology and validity testing, these personality tests have lost a notable amount of credibility. Focusing on questionnaire and behavioral based testing, this literature review aims to evaluate the significance of personality testing within the dog community. Each assessment will be analyzed for measurements and validity, as well as potential drawbacks and benefits. Four prominent personality assessments will be discussed in depth. These assessments include C-BARQ, DPQ, SAFER, and VIDOPET. I advocate for a mixed assessment model approach and highlight the benefits of expanding personality testing into genetic research.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.